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Drought forecasting and prediction is a complicated process due to thecomplexity and scalability of the environmental parameters involved. Hence, itrequired a high level of expertise to predict. In this paper, we describe theresearch and development of a rule-based drought early warning expert systems(RB-DEWES) for forecasting drought using local indigenous knowledge obtainedfrom domain experts. The system generates inference by using rule set andprovides drought advisory information with attributed certainty factor (CF)based on the user's input. The system is believed to be the first expert systemfor drought forecasting to use local indigenous knowledge on drought. Thearchitecture and components such as knowledge base, JESS inference engine andmodel base of the system and their functions are presented.

Towards the Development of a Rule-based Drought Early Warning Expert Systems using Indigenous Knowledge